Data-driven prediction of Air Traffic Controllers reactions to resolving conflicts

With the aim to enhance automation in conflict detection and resolution (CD&R) tasks in the Air Traffic Management domain, in this paper we propose deep learning techniques (DL) that can learn models of Air Traffic Controllers' (ATCO) reactions in resolving conflicts that can violate separation minimum constraints among aircraft trajectories: This implies learning when the ATCO will react towards resolving a conflict, and how he/she will react. Timely reactions, to which this paper aims, focus on when do reactions happen, aiming to predict the trajectory points, as the trajectory evolves, that the ATCO issues a conflict resolution action, while also predicting the type of resolution action (if any). Towards this goal, the paper formulates the ATCO reactions prediction problem for CD&R, and presents DL methods that can model ATCO timely reactions and evaluates these methods in real-world data sets, showing their efficacy in prediction with very high accuracy.

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